Public Abstract

DE-SC0008688: PREDICTIVE HIERARCHICAL MODELING OF CHEMICAL SEPARATIONS AND TRANSFORMATIONS IN FUNCTIONAL NANOPOROUS MATERIALS: Synergy of Electronic Structure Theory, Molecular Simulation, Machine Learning, and Exp

Award Status: Expired

Institution: Regents of the University of Minnesota, Minneapolis, MN
UEI: KABJZBBJ4B54
DUNS: 555917996

Most Recent Award Date: 08/01/2022
Number of Support Periods: 9
PM: Holder, Aaron

Current Budget Period: 09/01/2021 - 06/30/2023
Current Project Period: 09/01/2021 - 06/30/2023
PI: Siepmann, Joern Ilja

Supplement Budget Period: N/A

Public Abstract

Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Machine Learning, Electronic Structure Theory, Molecular Simulation, Electronic Structure Theory, and Experiment J. Ilja Siepmann, University of Minnesota−Twin Cities (Principal Investigator); Alan Aspuru-Guzik, Harvard University (co-Investigator); David C. Cantu, University of Nevada, Reno (co-Investigator); Coray M. Colina, University of Florida (co-Investigator); Laura Gagliardi, University of Chicago (co-Investigator); Jason D. Goodpaster, University of Minnesota−Twin Cities (co-Investigator); Christopher Knight, Argonne National Laboratory (co-Investigator); Sapna Sarupria, University of Minnesota−Twin Cities (co-Investigator); David S. Sholl, Georgia Institute of Technology (co-Investigator); Randall Q. Snurr, Northwestern University (co-Investigator); Donald G. Truhlar, University of Minnesota−Twin Cities (co-Investigator); Alvaro Vazquez-Mayagoitia, Argonne National Laboratory (co-Investigator); Jingyun Ye, Clarkson University (co-Investigator) This award supports the continuation of the Nanoporous Materials Genome Center (NMGC) with the University of Minnesota as the lead institution. Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the BES mission and objectives. The team of NMGC researchers will develop exascale-ready software, as well as improve computational/theoretical chemistry methods and data-driven science approaches that will enable (i) the de-novo design of functional NPMs for separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical framework structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs. The proposed research will be directed toward chemical separations and catalysis for complex multi-component mixtures. The NPM models will extend beyond currently accessible crystalline and/or homogeneous chemical systems to include hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces. The proposed research activities include: development and application of machine learning of adsorption in NPMs and of related properties; advances in quantum mechanical methods and force fields; workflows for efficient distributions of high-throughput simulations and porting of select simulation software for exascale computing resources; and data repositories for NPMs and associated properties. These computational tools will be applied for predictive modeling of: pollutant/ion adsorption from aqueous solution; adsorption of complex sorbent mixtures, and adsorption in defective/hierarchical sorbents; discovery of working fluid/sorbent pairs for adsorption cooling; investigations of cooperative effects from nodes, ligands, and solvent in MOF catalysis; understanding of electrocatalysis and photocatalysis in MOFs. The collaborative NMGC activities will also contribute to the training of postdoctoral associates, graduate students, and undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.

Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Machine Learning, Electronic Structure Theory, Molecular Simulation, Electronic Structure Theory, and Experiment

J. Ilja Siepmann, University of Minnesota−Twin Cities (Principal Investigator); Alan Aspuru-Guzik, Harvard University (co-Investigator); David C. Cantu, University of Nevada, Reno (co-Investigator); Coray M. Colina, University of Florida (co-Investigator); Laura Gagliardi, University of Chicago (co-Investigator); Jason D. Goodpaster, University of Minnesota−Twin Cities (co-Investigator); Christopher Knight, Argonne National Laboratory (co-Investigator); Sapna Sarupria, University of Minnesota−Twin Cities (co-Investigator); David S. Sholl, Georgia Institute of Technology (co-Investigator); Randall Q. Snurr, Northwestern University (co-Investigator); Donald G. Truhlar, University of Minnesota−Twin Cities (co-Investigator); Alvaro Vazquez-Mayagoitia, Argonne National Laboratory (co-Investigator); Jingyun Ye, Clarkson University (co-Investigator)

This award supports the continuation of the Nanoporous Materials Genome Center (NMGC) with the University of Minnesota as the lead institution. Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the BES mission and objectives. The team of NMGC researchers will develop exascale-ready software, as well as improve computational/theoretical chemistry methods and data-driven science approaches that will enable (i) the de-novo design of functional NPMs for separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical framework structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs. The proposed research will be directed toward chemical separations and catalysis for complex multi-component mixtures. The NPM models will extend beyond currently accessible crystalline and/or homogeneous chemical systems to include hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces.

The proposed research activities include: development and application of machine learning of adsorption in NPMs and of related properties; advances in quantum mechanical methods and force fields; workflows for efficient distributions of high-throughput simulations and porting of select simulation software for exascale computing resources; and data repositories for NPMs and associated properties. These computational tools will be applied for predictive modeling of: pollutant/ion adsorption from aqueous solution; adsorption of complex sorbent mixtures, and adsorption in defective/hierarchical sorbents; discovery of working fluid/sorbent pairs for adsorption cooling; investigations of cooperative effects from nodes, ligands, and solvent in MOF catalysis; understanding of electrocatalysis and photocatalysis in MOFs.

The collaborative NMGC activities will also contribute to the training of postdoctoral associates, graduate students, and undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.